T. Raschka

Artificial intelligence-based clustering and characterization of Parkinson's disease trajectories

C. Birkenbihl, A. Avbersek, N. Massat, 2022, Scientific Reports.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization

M. Peitsch, R. Eils, H. Fröhlich, 2021, Scientific Reports.

AI reveals insights into link between CD33 and cognitive impairment in Alzheimer’s Disease

H. Fröhlich, C. Ebeling, M. Sood, 2022, bioRxiv.

A machine learning method for the identification and characterization of novel COVID-19 drug targets

Sepehr Golriz Khatami, J. Kohlhammer, M. Hofmann-Apitius, 2023, Scientific reports.

Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment

Mohammad Asif Emon, Martin Hofmann-Apitius, Reagon Karki, 2017, Bioinform..

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

M. Hofmann-Apitius, Rebeca Queiroz Figueiredo, Tamara Raschka, 2021, bioRxiv.

Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

M. Hofmann-Apitius, Rebeca Queiroz Figueiredo, S. D. del Ser, 2022, BMC Bioinformatics.

Unraveling progression subtypes in people with Huntington’s disease

H. Gassner, F. Marxreiter, T. Raschka, 2024, The EPMA journal.

NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease

Martin Hofmann-Apitius, Tamara Raschka, Philipp Senger, 2016, Journal of Biomedical Semantics.